A comparative analysis of rule-based, neural network, and statistical classification systems for the bond rating problem
Multivariate data analysis (4th ed.): with readings
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A case-based approach using inductive indexing for corporate bond rating
Decision Support Systems - Decision-making and E-commerce systems
Inductive Learning for Risk Classification
IEEE Expert: Intelligent Systems and Their Applications
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Predicting bond ratings using publicly available information
Expert Systems with Applications: An International Journal
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Soft computing system for bank performance prediction
Applied Soft Computing
Predicting financial activity with evolutionary fuzzy case-based reasoning
Expert Systems with Applications: An International Journal
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
Support vector machines for credit scoring and discovery of significant features
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mining the customer credit using hybrid support vector machine technique
Expert Systems with Applications: An International Journal
Estimation of Rock Mass Rating System with an Artificial Neural Network
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Improving the generalization performance of RBF neural networks using a linear regression technique
Expert Systems with Applications: An International Journal
Application of a 3NN+1 based CBR system to segmentation of the notebook computers market
Expert Systems with Applications: An International Journal
Credit rating method with heterogeneous information
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Are rating agencies' assignments opaque? Evidence from international banks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Municipal credit rating modelling by neural networks
Decision Support Systems
Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model
Expert Systems with Applications: An International Journal
A stable credit rating model based on learning vector quantization
Intelligent Data Analysis
Expert Systems with Applications: An International Journal
Computers and Operations Research
Expert Systems with Applications: An International Journal
A logical analysis of banks' financial strength ratings
Expert Systems with Applications: An International Journal
Asset portfolio optimization using support vector machines and real-coded genetic algorithm
Journal of Global Optimization
Expert Systems with Applications: An International Journal
A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures
Computational Economics
Hi-index | 12.07 |
Corporate credit rating analysis has drawn a lot of research interests in previous studies, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the corporate credit rating problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, the researcher uses a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of RBF kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, the researcher compares its performance with those of multiple discriminant analysis (MDA), case-based reasoning (CBR), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.